HAGDAVS Dataset

Detection and Semantic Segmentation of vehicles in drone aerial orthomosaics has applications in different fields like security, traffic and parking management, urban planning, logistics, and transportation, among many others. This paper presents the HAGDAVS dataset fusing RGB spectral channel and Digital Surface Model DSM for the detection and segmentation of vehicles from aerial drone images including three vehicle classes: car, motorcycle, and ghosts (motorcycle or car). We supply DSM as an additional variable to be included in deep learning and computer vision models for increasing its accuracy. RGB orthomosaic, RG-DSM fusion, and multi-label mask are provided in Tag Image File Format. Geo-located vehicle bounding boxes are provided in GeoJSON vector format. It also describes the acquisition of drone data, the derived products, and the workflow to produce the dataset. Researchers would benefit from using the proposed dataset to improve results in the case of vehicle occlusion, geo-location, and the need for cleaning ghost vehicles. As far as we know, this is the first openly available dataset for vehicle detection and segmentation, comprising RG-DSM drone data fusion, and different color masks for motorcycles, cars, and ghosts.无人机航拍正射影像中的车辆检测与语义分割技术，具备安防、交通与停车管理、城市规划、物流、交通运输等诸多领域的应用价值。本文提出HAGDAVS数据集，该数据集融合RGB光谱通道与数字表面模型（Digital Surface Model, DSM），用于无人机航拍影像中的车辆检测与语义分割，涵盖三类目标：轿车、摩托车以及鬼影目标（摩托车或轿车）。本数据集将DSM作为额外特征变量纳入深度学习与计算机视觉模型的训练流程，以提升模型的检测与分割精度。RGB正射影像、RG-DSM融合数据以及多标签掩码均以标签图像文件格式（Tag Image File Format, TIFF）存储并提供。携带地理定位信息的车辆边界框以GeoJSON矢量格式提供。本数据集还涵盖了无人机数据采集、衍生产品生成以及数据集制作的完整流程。研究人员可通过本数据集，优化车辆遮挡、地理定位以及鬼影目标清理场景下的模型性能。据我们所知，本数据集是首个公开可用的融合RG-DSM无人机数据的车辆检测与语义分割数据集，包含针对摩托车、轿车与鬼影目标的差异化色彩掩码。

搜集汇总

背景与挑战

背景概述

HAGDAVS数据集是一个用于无人机航拍正射影像中车辆检测和语义分割的公开数据集，融合了RGB光谱通道和数字表面模型（DSM），以提高深度学习模型准确性，并涵盖汽车、摩托车和'ghosts'三类车辆。数据集提供TIFF格式的RGB正射影像、RG-DSM融合图像和多标签掩码，以及GeoJSON格式的地理定位边界框，适用于安全、交通管理等领域的研究，特别有助于解决车辆遮挡和地理定位问题。